###Markov chain Monte Carlo algorithms in computational genomics
Project ID: 2228bd1004 (You will need this ID for your application)
Research Theme: Mathematical Sciences
UCL Lead department: Division of Biosciences
Lead Supervisor: Ziheng Yang
Project Summary:
Genomic sequence data are experiencing explosive growth, and providing opportunities for addressing a number of exciting problems in evolutionary biology, such as inference of species phylogeneties, delimiting species boundaries, and inferring gene flow between species. While genomic data contain rich information about the history of species divergence and gene flow, extracting this information requires advanced probabilistic modeles and efficienct inference methods and computational algorithms. In the past two decades, the multispecies coalescent model has emerged as the natural framework for analysis of genomic data from closely related species. In this project the student will develop Bayesian inference methods and Markov chain Monte Carlo algorithms for inference under the multispecies coalescent model using genomic sequence data from closely related species. The project is multi-disciplinary and involves expertise in Markov chain theory, statistical inference, Bayesian computation (in particular MCMC), population genetics, and computational genomics. We expect the student to have a BSc in one of those areas and will receive training in the other areas.